جستجوی مقالات مرتبط با کلیدواژه « copy-move forgery » در نشریات گروه « برق »

Image is a powerful communication tool that is widely used in various applications, such as forensic medicine and court, where the validity of the image is crucial. However, with the development and availability of image editing tools, image manipulation can be easily performed for a specific purpose. Copy-move forgery is one of the simplest and most common methods of image manipulation. There are two traditional methods to detect this type of forgery: block-based and key point-based. In this study, we present a hybrid approach of block-based and key point-based methods using meta-heuristic algorithms to find the optimal configuration. For this purpose, we first search for pair blocks suspected of forgery using the genetic algorithm with the maximum number of matched key points as the fitness function. Then, we find the accurate forgery blocks using simulating annealing algorithm and producing neighboring solutions around suspicious blocks. We evaluate the proposed method on CoMoFod and COVERAGE datasets, and obtain the results of accuracy, precision, recall and IoU with values of 96.87, 92.15, 95.34 and 93.45 respectively. The evaluation results show the satisfactory performance of the proposed method.

Increased popularity of digital media and image editing software has led to the spread of multimedia content forgery for various purposes. Undoubtedly, law and forensic medicine experts require trustworthy and non-forged images to enforce rights. Copy-move forgery is the most common type of manipulation of digital images. Copy-move forgery is used to hide an area of the image or to repeat a portion in the same image. In this paper, a method is presented for detecting copy-move forgery using the Scale-Invariant Feature Transform (SIFT) algorithm. The spearman relationship and ward clustering algorithm are used to measure the similarity between key-points, also to increase the accuracy of forgery detection. This method is invariant to changes such as rotation, scale change, deformation, and light change; it falls into the category of blind forgery detection methods. The experimental results show that with its high resistance to apparent changes, the proposed method correctly detects 99.56 percent of the forged images in the dataset and reveals the forged areas.